| 应用近红外光谱估测小麦叶片氮含量 |
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| 引用本文： | 姚霞,汤守鹏,曹卫星,田永超,朱艳.应用近红外光谱估测小麦叶片氮含量[J].植物生态学报,2011,35(8):844-852. |
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| 作者姓名： | 姚霞 汤守鹏 曹卫星 田永超 朱艳 |
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| 作者单位： | 南京农业大学/国家信息农业工程技术中心, 江苏省信息农业高技术研究重点实验室, 南京 210095 |
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| 基金项目： | 国家“863”资助项目(2011AA100703); 国家自然科学基金(30871448); 教育部新世纪优秀人才支持计划(NCET-08-0797); 江苏省创新学者攀登计划(BK20081479); 江苏省自然科学基金(BK2008330和BK2010453)共同资助 |
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| 摘 要： | 研究利用近红外光谱(near-infrared, NIR)和化学计量学方法估测小麦(Triticum aestivum)新鲜叶片和粉末状干叶中全氮含量的可行性, 并建立小麦叶片氮含量估测模型, 以期为小麦氮素营养的精确管理提供理论依据。以3个小麦田间试验观测资料为基础, 分别运用偏最小二乘法(partial least squares, PLS)、反向传播神经网络(back-propagation neural network, BPNN)和小波神经网络(wavelet neural network, WNN), 建立小麦叶片氮含量的鲜叶和粉末状干叶近红外光谱估测模型, 用随机选择的样品集对所建模型进行测试和检验。结果显示, 利用PLS、BPNN和WNN 3种方法构建的近红外光谱模型均能准确地估测小麦叶片氮含量, 其中基于BPNN和WNN的模型优于基于PLS的模型, 且以基于WNN的模型表现最好。对模型进行检验的结果显示, 粉末状干叶模型的预测均方根误差(RMSEP)分别为0.147、0.101和0.094, 鲜叶模型的RMSEP分别为0.216、0.175和0.169, 模型的相关系数均在0.84以上。因此, 利用近红外光谱估算小麦叶片氮素营养精确可行, 对其他作物的氮素营养估测提供了借鉴和参考。
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| 关 键 词： | 叶片 近红外光谱 神经网络 偏最小二乘法 氮含量 小麦 |
| 收稿时间： | 2011-04-11 |
| 修稿时间： | 2011-05-31 |
Estimating the nitrogen content in wheat leaves by near-infrared reflectance spectroscopy |
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| YAO Xia,TANG Shou-Peng,CAO Wei-Xing,TIAN Yong-Chao,ZHU Yan.Estimating the nitrogen content in wheat leaves by near-infrared reflectance spectroscopy[J].Acta Phytoecologica Sinica,2011,35(8):844-852. |
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| Authors: | YAO Xia TANG Shou-Peng CAO Wei-Xing TIAN Yong-Chao ZHU Yan |
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| Affiliation: | National Engineering and Technology Center for Information Agriculture / Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University,
Nanjing 210095, China |
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| Abstract: | Aims Our objectives were to determine the feasibility of estimating nitrogen content in fresh and dry wheat leaves using near-infrared (NIR) spectroscopy and chemometrics and to establish the near-infrared model for estimating nitrogen content in wheat leaves in order to lay a foundation for wheat nitrogen management.
Methods We conducted three field experiments with different years, wheat varieties and nitrogen rates and determined time-course near-infrared absorbance spectroscopy and total nitrogen content from fresh and dry wheat leaves. The methods of partial least squares (PLS), back-propagation neural network (BPNN) and wavelet neural network (WNN) were used to establish the calibration models, and a dataset selected at random was used to evaluate the established models.
Important findings Near infrared calibration models based on PLS, BPNN and WNN could be used to estimate nitrogen content in wheat leaves with high precision and stable performance, especially WNN. The validation results showed that the root mean square errors of prediction (RMSEP) for the power model are 0.147, 0.101 and 0.094, respectively, while those for the fresh leaves model are 0.216, 0.175 and 0.169, respectively. The correlation coefficients (R2) for all models are >0.84. Therefore, near-infrared spectrometry can be an efficient method to estimate the nitrogen nutrition of crops. |
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| Keywords: | leaf near-infrared spectroscopy neural network partial least squares total nitrogen content wheat |
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